SVP Detection Using Machine Learning

N/AActive Not RecruitingNCT05731765

King's College London210 enrolled

Overview

This diagnostic study will use 410 retrospectively captured fundal videos to develop ML systems that detect SVPs and quantify ICP. The ground truth will be generated from the annotations of two independent, masked clinicians, with arbitration by an ophthalmology consultant in cases of disagreement.

Study Type

OBSERVATIONAL

Enrollment

210

Conditions

Intracranial Pressure Increase

Interventions

Machine Learning ModelDIAGNOSTIC_TEST

Automated machine learning system for the detection of spontaneous venous pulsations and quantification of intracranial pressure

Eligibility

Sex: ALLMin age: 18 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: * Patients aged ≥18 years with presumed normal ICP undergoing routine dilated OCT scans. * Patients undergoing a LP or continuous ICP monitoring with implanted transcranial pressure transducer devices at in- or out-patient neurology, neurosurgery or neuro-ophthalmology services. Exclusion Criteria: * Glaucoma diagnosis or glaucoma suspects in either eye. * Bilateral restricted fundal view, e.g. advanced bilateral cataracts. * Bilateral retinal vein or artery occlusion.

Locations (1)

King's College London

London, United Kingdom

Outcomes

Primary Outcomes

Area-under-the receiver operating characteristic (AUROC) for spontaneous venous pulsations detection

Binary classification performance of the machine learning model

Time frame: 1 year

Secondary Outcomes

Localisation of spontaneous venous pulsations

Bounding box overlap for the machine learning model

Time frame: 1 year

Quantification of intracranial pressure

Mean absolute error for the prediction of the intracranial pressure

Time frame: 1 year

Data from ClinicalTrials.gov

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